CN101930607A - Method for judging quality of image - Google Patents
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Abstract
The invention discloses a method for judging the quality of an image. The image is subjected to multilayer wavelet decomposition and each layer of high-frequency sub-band HH(n) is subjected to band-pass analysis, so the quality of the image can be effectively distinguished. The whole image quality evaluation is divided into the following five processing links of: performing wavelet decomposition on a two-dimensional image; quantizing high-frequency wavelet coefficients; counting histograms of wavelet quantization coefficients; calculating image quality evaluation coefficients; and grading the quality of the image. A reference image does not need to be introduced in the process of evaluating the quality of the image, so application is more convenient; and the selection of a grading threshold value is independent of the content of the image, grading characteristic is stable and adaptability is higher.
Description
Technical field
The invention belongs to a kind of intelligent perception technology in the digital image processing field, the method that specifically is used for judging quality of image, this method adopts carries out automatic classification to the method for image wavelet coefficient statistical study to picture quality, has a wide range of applications aspect intelligent video analysis.
Background technology
In automated graphics identification, intelligent video analysis field, the working environment of image identification system is often changeable and unstable, can receive some low-quality images, these low-quality images are meant: contain much noise, the fuzzy image that does not see, this is mainly unreasonable owing to the focal length of camera adjustment, reasons such as illumination is not enough, electronic interferences cause, these interference all will influence the operate as normal of system, even have a strong impact on the system identification effect.Therefore, detect the image that is interfered, effectively work most important for automatic system localization of fault, safeguards system.
Aspect image quality measure, be divided into following three class methods at present:
1, entirely with reference to algorithm: basic ideas are the local difference of coming comparison distorted image and reference picture by design feature, obtain a total average statistics amount then on entire image, and this statistic and picture quality are associated.These class methods be divided into have based on the error statistics amount and and based on the algorithm of HVS model, main representative has square error (MSE) model, Y-PSNR (PSNR) etc. all to belong to these class methods.This class methods search time is the longest, and is also ripe; But owing to need compared pixels level difference, so calculated amount is bigger, such algorithm is owing to need reference diagram, very flexible during application in addition.
2, half with reference to algorithm: basic ideas are at first with image block, are the correlated characteristic that unit adds up distorted image and reference picture respectively then with the image block, at last the difference between these statistical natures relatively.These class methods are divided into again based on the algorithm of characteristics of image statistic with based on the algorithm of digital watermarking, and main representative has structural similarity (SSIM) model, NSS model, VIF algorithm.
The characteristics of this class algorithm are that its need extract the part statistic and are used for comparison from reference picture, need not the information of original pixel scale, and data volume is littler with reference to algorithm more entirely, and calculation cost is littler;
With complete the same with reference to algorithm, these class methods still need reference picture, very flexible during application.
3, do not have with reference to algorithm: mainly comprise at the algorithm of type of distortion with based on the algorithm of machine learning,
The representative method has frequency domain evaluation algorithms, cycle reverses to propagate (CBP) neural network.The characteristics of these class methods need not reference picture, use extensivelyr, and generalization ability is stronger, does not have the focus that has begun to become research with reference to algorithm in recent years.But present nothing can't be broken away from the influence that its evaluation result is subjected to picture material with reference to algorithm.
But because the factor of interfering picture is more and picture material is changeable, above-mentioned image processing method all can not reach satisfied effect.
Summary of the invention
The difficult point more at the factor of interfering picture and picture material is changeable, the present invention does not propose a kind of image quality evaluation disposal route on having with reference to the basis of algorithm, this method is by the wavelet sub-band statistical nature of analysis image, reduce the influence of evaluation algorithms to a certain extent, applicable to the differentiation of different resolution picture, picture quality to picture material.
Thus, the purpose of this invention is to provide a kind of method for judging quality of image, this method is for video monitoring system, regularly the various images to each web camera collection gained in the system carry out quality analysis, picture quality is classified as: fuzzy do not see image, have much noise image, picture rich in detail, all analysis results are carried out statistical summaries.
Therefore, basic thought of the present invention is as follows: because the blurred picture radio-frequency component is less; It is more to contain much noise image radio-frequency component; And the radio-frequency component of picture rich in detail characteristics between blurred picture and noise image mostly, image is carried out the multilayer wavelet decomposition, and to each floor height frequently diagonal angle subband HH (n) (n layer diagonal high-frequency components) be with the reduction of fractions to a common denominator to analyse, can effectively distinguish out the picture quality quality.Whole image quality evaluation is divided into following five processing links: two dimensional image wavelet decomposition, high frequency wavelet coefficient quantization, small echo quantization parameter statistics with histogram, image quality evaluation coefficient calculations, picture quality classification.
For reaching above-mentioned order ground, implementation procedure of the present invention is as follows:
Step 1. pair image carries out 2-d wavelet and decomposes: select any one of haar wavelet basis, DD wavelet basis, SYM wavelet basis or COIF wavelet basis for use, with picture breakdown is the n layer, n 〉=3 correspond respectively in image blurring, clear picture, the image and contain three kinds of situations of much noise;
Image after pair of every layer decomposition of step 2. carries out the high frequency wavelet sub-band coefficients and quantizes, and the sub-band coefficients of n layer diagonal angle subband HH is quantized to space [0 255], and quantitative formula is:
Min (HH (n)) is the minimum value of n layer diagonal angle subband HH sub-band coefficients, max (HH (n)) is the maximal value of n layer diagonal angle subband HH sub-band coefficients, HH (i, j, n) pixel of expression n layer diagonal angle subband HH, F (i, j n) is HH (i, j, n) result after the quantification, its scope [0 12 ... 255];
Step 3. couple above-mentioned quantized result F carries out statistics with histogram;
Step 4. is chosen interval [T1, T2] according to histogrammic statistics, and according to [T1, T2] computed image quality bandwidth factor; Described interval [T1, T2] is by T1=μ-σ, and T2=μ+σ calculating gets, and wherein σ is a variance, and μ is the n floor height histogrammic average of small echo frequently; Computed image quality bandwidth factor: high frequency wavelet sub-band coefficients quantized result distribution approximate Gaussian distribution, comprised histogram 95% above energy on interval [T1, T2], according to [T1, T2] computed image quality bandwidth factor, concrete grammar is as follows:
Step 4.1 is calculated the n floor height histogrammic average of small echo frequently:
Wherein:
The probability that P (i) expression high frequency wavelet quantization parameter i occurs, the frequency that F (i) expression quantization parameter i occurs, the sum of all pixels of num presentation video;
Step 4.2 is determined picture quality bandwidth factor S:
(1) makes variances sigma=1, calculating energy
(2) if power＞95%, algorithm convergence, T1=μ-σ, T2=μ+σ changeed for (4) step;
(3) (1) step was changeed in σ=σ+1;
(4) calculate S (n)=| T2-T1|.
Step 5.n=n+1, if n＜3, algorithm goes to step 2; Otherwise go to step 6;
Step 6. picture quality classification: utilize S (n) weighted sum that calculates in above-mentioned to obtain S
Total,
Step 6.1 is calculated
Get a
1=0.5, a
2=0.3, a
3=0.2.
The classification of step 6.2 picture quality is as follows:
(i). work as S
Total≤ S
0The time, image blurring;
(ii). work as S
0＜S
Total＜S
1, clear picture;
(iii). work as S
Total〉=S
1, contain much noise in the image.
According to the image of different resolution, image diagonal angle subband statistical nature is distributed, get S
0=35, S
1=70.
Compare with classic method, the present invention has following advantage:
1, in the image quality evaluation process, need not introduce reference picture, use conveniently, handle simpler and more direct; Picture material is chosen and do not relied on to classification thresholds, and graded features is stable, and adaptability is stronger.
2, wavelet coefficient is quantized and carry out statistics with histogram, not only reduced the bandwidth of image feature data, and more helped extracting global characteristics stable in the image.
3, quantitatively provide picture quality classification computing formula, can satisfy the particular division demand by suitable adjustment classification thresholds in actual applications.
Description of drawings
Fig. 1 is wavelet coefficient quantitative statistics figure, and among the figure, the wavelet coefficient after horizontal ordinate is represented to quantize, ordinate are represented the number of times that coefficient takes place;
Fig. 2 judging quality of image algorithm flow chart;
Fig. 3 judging quality of image algorithm application hardware structural drawing in video monitoring system.
Embodiment
Below, in conjunction with the accompanying drawings shown in, concrete enforcement of the present invention is elaborated.
Embodiment 1: on the matlab7.1 platform, adopt the matlab programming language to carry out emulation experiment, picture under the multiple environment handled, comprise image blurring, contain a large amount of white noises, three kinds of situations of clear picture.
Concrete processing procedure is described below (see figure 2):
Step 1. 2-d wavelet decomposes: selecting the haar wavelet basis for use, is n layer (getting n=3 in the experiment) with picture breakdown;
The n layer diagonal angle subband HH sub-band coefficients of step 2. after with the high frequency wavelet conversion quantizes: described sub-band coefficients is quantized to space [0 255], and quantitative formula is:
Min (HH (n)) is the minimum value of n layer diagonal angle subband HH sub-band coefficients, max (HH (n)) is the maximal value of n layer diagonal angle subband HH sub-band coefficients, HH (i, j, n) pixel of expression n layer diagonal angle subband HH, F (i, j n) is HH (i, j, n) result after the quantification, its scope [0 12 ... 255];
Step 3. couple above-mentioned quantized result F carries out statistics with histogram, the result as shown in Figure 1, transverse axis is represented quantization parameter, the longitudinal axis is represented the number of times that coefficient occurs;
Step 4. computed image quality bandwidth factor: describe according to Fig. 1, high frequency wavelet sub-band coefficients quantized result distribution approximate Gaussian distribution has comprised histogram 96% above energy on interval [T1, T2], according to the bandwidth factor of this statistic histogram:
Step 4.1 is calculated the n floor height histogrammic average of small echo frequently:
Wherein:
The probability that P (i) expression high frequency wavelet quantization parameter i occurs, the frequency that F (i) expression quantization parameter i occurs, the sum of all pixels of num presentation video.
Step 4.2 is determined picture quality bandwidth factor S:
(1) makes variances sigma=1, calculating energy
(2) if power＞95%, algorithm convergence, T1=μ-σ, T2=μ+σ changeed for (4) step;
(3) (1) step was changeed in σ=σ+1;
(4) calculate S (n)=| T2-T1|.
Step 5.n=n+1, if n＜3, algorithm goes to step 2; Otherwise go to step 6;
Step 6. picture quality classification: utilize S (n) weighted sum that calculates in above-mentioned to obtain S
Total,
Step 6.1 is calculated
Get a
1=0.5, a
2=0.3, a
3=0.2.
The classification of step 6.2 picture quality is as follows:
(i). work as S
Total≤ S
0The time, image blurring;
(ii). work as S
0＜S
Total＜S
1, clear picture;
(iii). work as S
Total〉=S
1, contain much noise in the image.
In the experiment, according to the picture of different resolution, image diagonal angle subband statistical nature distributes, and gets S
0=35, S
1=70.
Process according to above-mentioned steps 1 to 6 calculates and divides the class quilt to picture, compare with classic method, and the present invention need not reference picture, uses simplyr, and the picture quality classification is more stable.
Embodiment 2: at vs2005, opencv1.0 environment programming is down realized the image quality analysis dynamic base, and it is joined server end in the video monitoring system.As shown in Figure 3, a networked video monitoring system, video server connects a plurality of video encoders by network, and a scrambler connects a plurality of video cameras.Video server regularly starts the image quality analysis module, multichannel camera review information is carried out quality analysis, and analysis result is gathered output.
According to flow process shown in Figure 2, image information is carried out quality analysis, can obtain satisfied quality analysis result equally.
Claims (7)
1. a method for judging quality of image is characterized in that this method comprises the steps:
Step 1 is carried out 2-d wavelet to image and is decomposed;
Step 2 is carried out the subband band reduction of fractions to a common denominator frequently of each floor height with the image after decomposing and is analysed;
Step 3 is analysed the result with the described band reduction of fractions to a common denominator and is carried out statistics with histogram;
Step 4, computed image quality bandwidth factor;
Step 5 is carried out the picture quality classification by the picture quality bandwidth factor that calculates.
2. method for judging quality of image as claimed in claim 1, it is characterized in that in step 1, image is carried out 2-d wavelet to be decomposed: selecting any one of haar wavelet basis, DD wavelet basis, SYM wavelet basis or COIF wavelet basis for use, is the n layer with picture breakdown, n 〉=3.
3. method for judging quality of image as claimed in claim 1 is characterized in that in step 2, the image after every layer of decomposition is carried out the high frequency wavelet sub-band coefficients quantize, and the sub-band coefficients of n layer diagonal angle subband HH is quantized to space [0 255], and quantitative formula is:
Min (HH (n)) is the minimum value of n layer diagonal angle subband HH sub-band coefficients, max (HH (n)) is the maximal value of n layer diagonal angle subband HH sub-band coefficients, HH (i, j, n) pixel of expression n layer diagonal angle subband HH, F (i, j n) is HH (i, j, n) result after the quantification, its scope [0 12 ... 255].
4. method for judging quality of image as claimed in claim 1 is characterized in that in step 4, according to histogrammic statistics, chooses interval [T1, T2], and according to [T1, T2] computed image quality bandwidth factor.
5. method for judging quality of image as claimed in claim 4 is characterized in that described interval [T1, T2], is by T1=μ-σ, and T2=μ+σ calculating gets, and wherein σ is a variance, and μ is the n floor height histogrammic average of small echo frequently.
6. method for judging quality of image as claimed in claim 5 is characterized in that the account form of picture quality bandwidth factor is:
A, calculate the n floor height histogrammic average of small echo frequently by following formula:
Wherein:
The probability that P (i) expression high frequency wavelet quantization parameter i occurs, the frequency that F (i) expression high frequency wavelet quantization parameter i occurs, the sum of all pixels of num presentation video;
B, determine picture quality bandwidth factor S
(1) makes variances sigma=1, calculating energy
(2) if power＞95%, algorithm convergence, T1=μ-σ, T2=μ+σ changeed for (4) step;
(3) (1) step was changeed in σ=σ+1;
(4) calculate S (n)=| T2-T1|.
7. method for judging quality of image as claimed in claim 6 is characterized in that in step 5, and the differentiation stage division that picture quality is concrete is as follows:
I, utilize S (n) weighted sum that calculates in above-mentioned to obtain S
Total,
Calculate
Get a
1=0.5, a
2=0.3, a
3=0.2,
Ii, picture quality classification are as follows:
(i). work as S
Total≤ S
0The time, image blurring;
(ii). work as S
0＜S
Total＜S
1, clear picture;
(iii). work as S
Total〉=S
1, contain much noise in the image.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102496162A (en) * | 2011-12-21 | 2012-06-13 | 浙江大学 | Method for evaluating quality of part of reference image based on non-tensor product wavelet filter |
| CN103686148A (en) * | 2013-12-05 | 2014-03-26 | 北京华戎京盾科技有限公司 | Automatic video image resolution detecting method based on image processing |
| CN104036498A (en) * | 2014-05-28 | 2014-09-10 | 杭州电子科技大学 | Fast evaluation method of OCT image quality based on layer by layer classification |
| CN105118057A (en) * | 2015-08-18 | 2015-12-02 | 江南大学 | Image sharpness evaluation method based on quaternion wavelet transform amplitudes and phase positions |
| CN107689039A (en) * | 2016-08-05 | 2018-02-13 | 同方威视技术股份有限公司 | Estimate the method and apparatus of image blur |
| CN108985351A (en) * | 2018-06-27 | 2018-12-11 | 北京中安未来科技有限公司 | It is a kind of that the method and apparatus of blurred picture are identified based on gradient direction sparse features information, calculate equipment and storage medium |
| CN109523533A (en) * | 2018-11-14 | 2019-03-26 | 北京奇艺世纪科技有限公司 | A kind of image quality evaluating method and device |
| CN113393461A (en) * | 2021-08-16 | 2021-09-14 | 北京大学第三医院（北京大学第三临床医学院） | Method and system for screening metaphase chromosome image quality based on deep learning |
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102496162A (en) * | 2011-12-21 | 2012-06-13 | 浙江大学 | Method for evaluating quality of part of reference image based on non-tensor product wavelet filter |
| CN103686148A (en) * | 2013-12-05 | 2014-03-26 | 北京华戎京盾科技有限公司 | Automatic video image resolution detecting method based on image processing |
| CN103686148B (en) * | 2013-12-05 | 2015-09-09 | 北京华戎京盾科技有限公司 | A kind of method of the automatic detection video image clarity based on Digital Image Processing |
| CN104036498A (en) * | 2014-05-28 | 2014-09-10 | 杭州电子科技大学 | Fast evaluation method of OCT image quality based on layer by layer classification |
| CN104036498B (en) * | 2014-05-28 | 2017-01-11 | 杭州电子科技大学 | Fast evaluation method of OCT image quality based on layer by layer classification |
| CN105118057A (en) * | 2015-08-18 | 2015-12-02 | 江南大学 | Image sharpness evaluation method based on quaternion wavelet transform amplitudes and phase positions |
| CN107689039A (en) * | 2016-08-05 | 2018-02-13 | 同方威视技术股份有限公司 | Estimate the method and apparatus of image blur |
| CN107689039B (en) * | 2016-08-05 | 2021-01-26 | 同方威视技术股份有限公司 | Method and device for estimating image fuzziness |
| CN108985351A (en) * | 2018-06-27 | 2018-12-11 | 北京中安未来科技有限公司 | It is a kind of that the method and apparatus of blurred picture are identified based on gradient direction sparse features information, calculate equipment and storage medium |
| CN108985351B (en) * | 2018-06-27 | 2021-11-26 | 北京中安未来科技有限公司 | Method and device for recognizing blurred image based on gradient direction sparse characteristic information, computing equipment and storage medium |
| CN109523533A (en) * | 2018-11-14 | 2019-03-26 | 北京奇艺世纪科技有限公司 | A kind of image quality evaluating method and device |
| CN113393461A (en) * | 2021-08-16 | 2021-09-14 | 北京大学第三医院（北京大学第三临床医学院） | Method and system for screening metaphase chromosome image quality based on deep learning |
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